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Learning the Parameters of Probabilistic Answer Set Programs

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Inductive Logic Programming (ILP 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13779))

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Abstract

Probabilistic Answer Set Programming (PASP) is a powerful formalism that allows to model uncertain scenarios with answer set programs. One of the possible semantics for PASP is the credal semantics, where a query is associated with a probability interval rather than a sharp probability value. In this paper, we extend the learning from interpretations task, usually considered for Probabilistic Logic Programming, to PASP: the goal is, given a set of (partial) interpretations, to learn the parameters of a PASP program such that the product of the lower bounds of the probability intervals of the interpretations is maximized. Experimental results show that the execution time of the algorithm is heavily dependent on the number of parameters rather than on the number of interpretations.

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Notes

  1. 1.

    Source code and datasets available at https://github.com/damianoazzolini/pasta.

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Acknowledgment

This research was partly supported by TAILOR, a project funded by EU Horizon 2020 research and innovation programme under GA No. 952215. Damiano Azzolini was supported by IndAM - GNCS Project with code CUP_E55F22000270001.

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Authors and Affiliations

  1. Dipartimento di Scienze dell’Ambiente e della Prevenzione, Università di Ferrara, Ferrara, Italy

    Damiano Azzolini

  2. Dipartimento di Ingegneria, Università di Ferrara, Ferrara, Italy

    Elena Bellodi

  3. Dipartimento di Matematica e Informatica, Università di Ferrara, Ferrara, Italy

    Fabrizio Riguzzi

Authors
  1. Damiano Azzolini
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  2. Elena Bellodi
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  3. Fabrizio Riguzzi
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Correspondence to Damiano Azzolini .

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Editors and Affiliations

  1. Imperial College London, London, UK

    Stephen H. Muggleton

  2. University of Surrey, Guildford, UK

    Alireza Tamaddoni-Nezhad

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Azzolini, D., Bellodi, E., Riguzzi, F. (2024). Learning the Parameters of Probabilistic Answer Set Programs. In: Muggleton, S.H., Tamaddoni-Nezhad, A. (eds) Inductive Logic Programming. ILP 2022. Lecture Notes in Computer Science(), vol 13779. Springer, Cham. https://doi.org/10.1007/978-3-031-55630-2_1

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  • DOI: https://doi.org/10.1007/978-3-031-55630-2_1

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